PDF Download Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing Full Description

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EBOOK [P.D.F] Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing Read Online Details

Details Product: Getting numbers is easy getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to * Use the scientific method to evaluate hypotheses using controlled experiments * Define key metrics and ideally an Overall Evaluation Criterion * Test for trustworthiness of the results and alert experimenters to violated assumptions * Build a scalable platform that lowers the marginal cost of experiments close to zero * Avoid pitfalls like carryover effects and Twyman's law * Understand how statistical issues play out in practice
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PDF Download Trustworthy Online Controlled

Experiments: A Practical Guide to A/B Testing

Full Description


Description PDF Download Trustworthy Online

Controlled Experiments: A Practical Guide to A/B

Testing Full Description

Getting numbers is easy getting numbers you can trust is hard. This practical guide

by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to

accelerate innovation using trustworthy online controlled experiments, or A/B tests.

Based on practical experiences at companies that each run more than 20,000

controlled experiments a year, the authors share examples, pitfalls, and advice for

students and industry professionals getting started with experiments, plus deeper

dives into advanced topics for practitioners who want to improve the way they make

data-driven decisions. Learn how to * Use the scientific method to evaluate

hypotheses using controlled experiments * Define key metrics and ideally an Overall

Evaluation Criterion * Test for trustworthiness of the results and alert experimenters

to violated assumptions * Build a scalable platform that lowers the marginal cost of

experiments close to zero * Avoid pitfalls like carryover effects and Twyman's law *

Understand how statistical issues play out in practice

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